class_docstr: Multi-Layer Perceptron with Batch Normalization.
  
  Args:
      in_size (int): Input size.
      out_size (int): Output size.
      hidden_sizes (list of ints): Sizes of hidden channels.
      normalize_input (bool): If set to True, Batch Normalization is applied
          to inputs.
      normalize_output (bool): If set to True, Batch Normalization is applied
          to outputs.
      nonlinearity (callable): Nonlinearity between layers. It must accept a
          Variable as an argument and return a Variable with the same shape.
          Nonlinearities with learnable parameters such as PReLU are not
          supported.
      last_wscale (float): Scale of weight initialization of the last layer.
code: def __init__(
      self,
      in_size,
      out_size,
      hidden_sizes,
      normalize_input=True,
      normalize_output=False,
      nonlinearity=F.relu,
      last_wscale=1,
  ):
      self.in_size = in_size
      self.out_size = out_size
      self.hidden_sizes = hidden_sizes
      self.normalize_input = normalize_input
      self.normalize_output = normalize_output
      self.nonlinearity = nonlinearity
  
      super().__init__()
      if normalize_input:
          self.input_bn = nn.BatchNorm1d(in_size)
  
      if hidden_sizes:
          self.hidden_layers = nn.ModuleList()
          self.hidden_layers.append(LinearBN(in_size, hidden_sizes[0]))
          for hin, hout in zip(hidden_sizes, hidden_sizes[1:]):
              self.hidden_layers.append(LinearBN(hin, hout))
          self.output = nn.Linear(hidden_sizes[-1], out_size)
      else:
          self.output = nn.Linear(in_size, out_size)
      init_lecun_normal(self.output.weight, scale=last_wscale)
  
      if normalize_output:
          self.output_bn = nn.BatchNorm1d(out_size)
  
file_path: pfrl/pfrl/nn/mlp_bn.py
imports: import torch.nn
  import torch.nn.functional
  from pfrl.initializers import init_lecun_normal
label_desc: Use image node_id 1 to create a new MLPBN object from inherited base classes: nn with example: obj = MLPBN(in_size, out_size, hidden_sizes, normalize_input, normalize_output, nonlinearity, last_wscale)